Gaulke Sarah M, Hohoff Tara, Rogness Brittany A, Davis Mark A
Illinois Natural History Survey, Prairie Research Institute University of Illinois Urbana-Champaign Champaign Illinois USA.
Ecol Evol. 2023 Jun 9;13(6):e10161. doi: 10.1002/ece3.10161. eCollection 2023 Jun.
Technological advances increase opportunities for novel wildlife survey methods. With increased detection methods, many organizations and agencies are creating habitat suitability models (HSMs) to identify critical habitats and prioritize conservation measures. However, multiple occurrence data types are used independently to create these HSMs with little understanding of how biases inherent to those data might impact HSM efficacy. We sought to understand how different data types can influence HSMs using three bat species (, , and ). We compared the overlap of models created from passive-only (acoustics), active-only (mist-netting and wind turbine mortalities), and combined occurrences to identify the effect of multiple data types and detection bias. For each species, the active-only models had the highest discriminatory ability to tell occurrence from background points and for two of the three species, active-only models preformed best at maximizing the discrimination between presence and absence values. By comparing the niche overlaps of HSMs between data types, we found a high amount of variation with no species having over 45% overlap between the models. Passive models showed more suitable habitat in agricultural lands, while active models showed higher suitability in forested land, reflecting sampling bias. Overall, our results emphasize the need to carefully consider the influences of detection and survey biases on modeling, especially when combining multiple data types or using single data types to inform management interventions. Biases from sampling, behavior at the time of detection, false positive rates, and species life history intertwine to create striking differences among models. The final model output should consider biases of each detection type, particularly when the goal is to inform management decisions, as one data type may support very different management strategies than another.
技术进步增加了采用新型野生动物调查方法的机会。随着检测方法的增多，许多组织和机构正在创建栖息地适宜性模型（HSMs），以确定关键栖息地并对保护措施进行优先排序。然而，多种出现数据类型被独立用于创建这些HSMs，而对这些数据固有的偏差如何影响HSMs的有效性却了解甚少。我们试图通过三种蝙蝠物种（、和）来了解不同数据类型如何影响HSMs。我们比较了仅基于被动（声学）、仅基于主动（雾网捕获和风力涡轮机致死率）以及综合出现情况创建的模型的重叠情况，以确定多种数据类型和检测偏差的影响。对于每个物种，仅基于主动的模型在区分出现点和背景点方面具有最高的判别能力，并且对于三种物种中的两种，仅基于主动的模型在最大化存在值和不存在值之间的区分方面表现最佳。通过比较不同数据类型的HSMs的生态位重叠情况，我们发现存在大量差异，没有一个物种的模型之间重叠超过45%。被动模型显示农业用地中有更适宜的栖息地，而主动模型显示森林用地中有更高的适宜性，这反映了抽样偏差。总体而言，我们的结果强调需要仔细考虑检测和调查偏差对建模的影响，特别是在组合多种数据类型或使用单一数据类型为管理干预提供信息时。抽样偏差、检测时的行为、假阳性率和物种生活史相互交织，导致模型之间存在显著差异。最终的模型输出应考虑每种检测类型的偏差，特别是当目标是为管理决策提供信息时，因为一种数据类型可能支持与另一种数据类型非常不同的管理策略。
